Measuring the Discrepancy between Conditional Distributions: Methods, Properties and Applications
Authors: Shujian Yu, Ammar Shaker, Francesco Alesiani, Jose Principe
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present three solid examples on machine learning applications to demonstrate the performance improvement in the state-of-the-art (SOTA) methodologies gained by our conditional divergence statistic. We evaluate the performance of our method against four SOTA error-based concept drift detectors (i.e., DDM [Gama et al., 2004], EDDM [Baena-Garcıa et al., 2006], HDDM [Fr ıas-Blanco et al., 2014], and PERM) on two realworld data streams, namely the Digits08 [Sethi and Kantardzic, 2017] and the Abrupt Insects [dos Reis et al., 2016]. |
| Researcher Affiliation | Collaboration | 1NEC Labs Europe, 69115 Heidelberg, Germany 2University of Florida, Gainesville, FL 32611, USA |
| Pseudocode | Yes | Algorithm 1 Test the conditional distribution divergence (CDD) based on the matrix Bregman divergence |
| Open Source Code | Yes | Code of our statistic is available at https: //bit.ly/Bregman Correntropy. |
| Open Datasets | Yes | To this end, we select data from 29 tasks that are collected from various landmine fields3. 3http://www.ee.duke.edu/ lcarin/Landmine Data.zip. We replace the na ıve ℓ2 distance with our proposed statistic to reconstruct the initial k NN graph for CCMTL and test its performance on a real-world Parkinson s disease data set [Tsanas et al., 2009]. We evaluate the performance of our method against four SOTA error-based concept drift detectors... on two realworld data streams, namely the Digits08 [Sethi and Kantardzic, 2017] and the Abrupt Insects [dos Reis et al., 2016]. We perform feature selection on two benchmark data sets [Brown et al., 2012]. |
| Dataset Splits | No | No explicit training/test/validation split percentages or counts that include a separate 'validation' set are provided. The paper mentions 'different train/test ratios' for some experiments and '10 fold cross-validation' for others, but no explicit validation split. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments are provided. |
| Software Dependencies | No | No specific ancillary software details (e.g., library or solver names with version numbers) are provided. The paper mentions various methods and estimators, such as 'adaptive k NN estimator' and 'linear Support Vector Machine (SVM)', but without corresponding version numbers for implementation reproducibility. |
| Experiment Setup | Yes | We then use Algorithm 1 (P = 500, η = 0.1) to test if our statistic can distinguish these two data sets. Throughout this work, we determine kernel width σ with the Silverman s rule of thumb [Silverman, 1986]. We select 10 features and use the linear Support Vector Machine (SVM) as the baseline classifier. |